


'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A__						:   Optional[Any]				=    {
    """configuration_mobilenet_v2""": [
        """MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileNetV2Config""",
        """MobileNetV2OnnxConfig""",
    ],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__						:   Union[str, Any]				=    ["""MobileNetV2FeatureExtractor"""]
      A__						:   Optional[int]				=    ["""MobileNetV2ImageProcessor"""]


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__						:   Tuple				=    [
          """MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MobileNetV2ForImageClassification""",
          """MobileNetV2ForSemanticSegmentation""",
          """MobileNetV2Model""",
          """MobileNetV2PreTrainedModel""",
          """load_tf_weights_in_mobilenet_v2""",
      ]


if TYPE_CHECKING:
      from .configuration_mobilenet_va import (
          MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
          MobileNetVaConfig,
          MobileNetVaOnnxConfig,
      )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
            from .image_processing_mobilenet_va import MobileNetVaImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mobilenet_va import (
                MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                MobileNetVaForImageClassification,
                MobileNetVaForSemanticSegmentation,
                MobileNetVaModel,
                MobileNetVaPreTrainedModel,
                load_tf_weights_in_mobilenet_va,
            )


else:
      import sys

      A__						:   List[Any]				=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)














"""simple docstring"""
import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property



@unittest.skipUnless(os.path.exists(a_)							,	"""Tatoeba directory does not exist.""")
class        UpperCamelCase__ (				unittest.TestCase):







    """simple docstring"""







    @cached_property
    def 				a__			(		self :	int ):





           '''simple docstring'''

           __magic_name__      				=    tempfile.mkdtemp()
           return TatoebaConverter(save_dir=UpperCamelCase_ )







    @slow
    def 				a__			(		self :	List[str] ):





           '''simple docstring'''

           self.resolver.convert_models(['heb-eng'] )







    @slow
    def 				a__			(		self :	Tuple ):





           '''simple docstring'''

           __magic_name__				,  __magic_name__      				=    self.resolver.write_model_card('opus-mt-he-en'				,    dry_run=UpperCamelCase_ )
           assert mmeta["long_pair"] == "heb-eng"